Integrating protein structure and genomic data to predict antibiotic resistance in Mycobacterium tuberculosis

整合蛋白质结构和基因组数据来预测结核分枝杆菌的抗生素耐药性

• 批准号: 10312207
• 负责人: Anna Gustafson Green
• 金额: $ 6.64万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2023-10-02
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10312207
• 关键词: 3-Dimensional; Active Sites; Address; Affect; Antibiotic Resistance; Antibiotics; Antitubercular Agents; Antitubercular Antibiotics; Bacteria; Bacterial Genes; Benchmarking; Biological; Cessation of life; Clinic; Clinical; Computer Analysis; Consumption; Crystallization; Data; Data Set; Development; Diagnosis; Diagnostic; Disease; Evolution; Frequencies; Genes; Genetic Markers; Genome; Genomics; Genotype; Knowledge; Lead; Ligand Binding; Location; M. tuberculosis genome; Maps; Measures; Methods; Microbe; Modeling; Molecular; Mutation; Mycobacterium tuberculosis; Pharmaceutical Preparations; Phenotype; Prevalence; Proteins; Proteome; Resistance; Shapes; Signal Transduction; Site; Source; Statistical Methods; Structure; Sum; Testing; Time; Tuberculosis; Variant; Work; base; fitness; genetic association; genetic variant; genomic data; human pathogen; microbial genome; pathogen; pathogenic bacteria; phenotypic data; protein function; protein structure; protein structure prediction; resistant strain; three dimensional structure; tool

Project Abstract Tuberculosis causes over one million deaths annually, and increasing antibiotic resistance is rendering the disease more difficult to treat. Rapid genotype-based resistance diagnosis of Mycobacterium tuberculosis, the bacterium that causes tuberculosis, is needed to overcome the long treatment delays associated with culture- based methods. Previous work has established sets of genetic markers of antibiotic resistance to more common antibiotics, but such studies require large numbers of sequenced resistant isolates, and are unable to make predictions for rare or newly observed variants. The requirement for large numbers of isolates is especially problematic for five newly introduced antitubercular agents, which have small but increasing numbers of documented resistant isolates. Traditional methods for associating genotype with phenotype assume that every site is independent, and therefore many examples of mutations at a particular site are needed to infer statistically significant effects of variants on phenotype. Biological knowledge tells us that this assumption is not true – most bacterial genes encode proteins, which have distinct three-dimensional shapes and functions. Mutations that causes changes in similar regions of a protein are more likely to have similar effects on phenotype, potentially allowing for sharing of statistical signal that could increase the power of significance testing. In this proposed project, I will develop two complimentary statistical approaches that will use protein three-dimensional structure to boost signal from genetic variants that cause antibiotic resistance in M. tuberculosis. Specifically, I will first develop an unsupervised statistical test to determine if repeated mutations within the same protein are clustered in three-dimensional space, which indicates that the mutations confer a fitness benefit. This approach will have increased sensitivity over traditional methods that look for significant numbers of mutations, and facilitate the development of mechanistic hypotheses about the effects of mutation on protein function. Second, I will use protein three-dimensional structure as a prior in a Bayesian linear mixed model to predict antibiotic resistance. This prior will allow nearby variants to ‘boost’ one another’s signal and establish associations between genotype and phenotype that are beyond the reach of current methods. The key application of this approach will be establishing resistance-conferring genotypes for five newly introduced antitubercular agents. The approach proposed here will likely generalize to other bacterial pathogens and represent an important leap forward in using pathogen molecular data in the clinic.

项目摘要 结核病每年导致超过一百万人死亡，抗生素耐药性的增加正在导致 这种疾病更难治疗。基于基因型的结核分枝杆菌耐药性快速诊断， 需要克服导致结核病的细菌，以克服与培养相关的长期治疗延误 先前的工作已经建立了一组更常见的抗生素耐药性的遗传标记。 抗生素，但此类研究需要大量测序的耐药菌株，并且无法确定 对罕见或新观察到的变异的预测尤其需要大量分离株。 对于五种新推出的抗结核药物来说存在问题，这些药物的数量虽小但数量不断增加 已记录的耐药菌株。 将基因型与表型关联的传统方法假设每个位点都是独立的，并且 因此，需要特定位点的许多突变例子来推断统计上显着的影响 生物学知识告诉我们，这种假设是不正确的——大多数细菌基因。 编码蛋白质，其具有独特的三维形状和功能，导致变化。 蛋白质的相似区域更有可能对表型产生相似的影响，从而可能允许共享 可以提高显着性检验能力的统计信号。 在这个拟议的项目中，我将开发两种使用蛋白质的互补统计方法 三维结构可增强来自导致分枝杆菌抗生素耐药性的遗传变异的信号。 具体来说，我将首先开发一个无监督的统计测试来确定是否存在重复突变。 同一蛋白质内的蛋白质聚集在三维空间中，这表明突变赋予了 与寻找显着健身益处的传统方法相比，这种方法将具有更高的敏感性。 突变的数量，并促进有关突变影响的机制假设的发展 其次，我将使用蛋白质三维结构作为贝叶斯线性混合的先验。 预测抗生素耐药性的模型将允许附近的变体“增强”彼此的信号并 建立基因型和表型之间的关联，这是当前方法无法达到的关键。 该方法的应用将为五种新引入的细菌建立赋予抗性的基因型 这里提出的方法可能会推广到其他细菌病原体和 使用病原体分子数据在临床中表示的重要飞跃。